Enabling Fast Ramp-Up of Assembly Lines through Context-Mapping of Implicit Operator Knowledge and Machine-Derived Data

Konrad, Konstantin; Hoffmeister, Michael; Zapp, Matthias; Verl, Alexander; Busse, Johannes

doi:10.1007/978-3-642-28163-1_20

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…The production engineer uses the CSM to create a SM for a specific production system, in which all the applicationdependent declarative knowledge is represented. One particular application of the CSM is structuring operator knowledge and relating it to machine data during the rampup of an assembly system [32]. Although the main use case of FRAME is assembly systems, the default CSM provided by FRAME can be extended to cater for different manufacturing processes.…”

Section: Semantic Modellingmentioning

confidence: 99%

Design of a self-learning multi-agent framework for the adaptation of modular production systems

Scrimieri

Afazov

Ratchev

2021

Int J Adv Manuf Technol

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This paper presents the design of a multi-agent framework that aids engineers in the adaptation of modular production systems. The framework includes general implementations of agents and other software components for self-learning and adaptation, sensor data analysis, system modelling and simulation, as well as human-computer interaction. During an adaptation process, operators make changes to the production system, in order to increase capacity or manufacture a product variant. These changes are automatically captured and evaluated by the framework, building an experience base of adjustments that is then used to infer adaptation knowledge. The architecture of the framework consists of agents divided in two layers: the agents in the lower layer are associated with individual production modules, whereas the agents in the higher layer are associated with the entire production line. Modelling, learning, and adaptations can be performed at both levels, using a semantic model to specify the structure and capabilities of the production system. An evaluation of a prototype implementation has been conducted on an industrial assembly system. The results indicate that the use of the framework in a typical adaptation process provides a significant reduction in time and resources required.

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Section: Semantic Modellingmentioning

confidence: 99%

Design of a self-learning multi-agent framework for the adaptation of modular production systems

Scrimieri

Afazov

Ratchev

2021

Int J Adv Manuf Technol

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“…Improving the aspect of knowledge capture and communication during ramp-up is an important research subject, helping to keep disturbances caused by the loss of human knowledge as small as possible. Yet few researchers propose a suitable learning approach and assisting tools (software) for ramp-up [21].…”

Section: Related Workmentioning

confidence: 99%

Towards a Decision-support Framework for Reducing Ramp-up Effort in Plug-and-Produce Systems

Zimmer

Ferreira

Danny

et al. 2019

2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS)

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Nowadays, shorter and more flexible production cycles are vital to meet the increasing customised product demand. As any delays and downtimes in the production towards time-to-market means a substantial financial loss, manufacturers take an interest in getting the production system to full utilisation as quickly as possible. The concept of plug-andproduce manufacturing systems facilitates an easy integration process through embedded intelligence in the devices. However, a human still needs to validate the functionality of the system and more importantly must ensure that the required quality and performance is delivered. This is done during the ramp-up phase, where the system is assembled and tested first-time. System adaptations and a lack of standard procedures make the ramp-up process still largely dependent on the operator's experience level. A major problem that currently occurs during ramp-up, is a loss of knowledge and information due to a lack of means to capture the human's experience. Acquiring this information can be used to simplify future ramp-up cases as additional insights about change actions and their effect on the system could be revealed. Hence, this paper proposes a decisionsupport framework for plug-and-produce assembly systems that will help to reduce the ramp-up effort and ultimately shorten ramp-up time. As an illustrative example, a glueing station developed as part of the European project openMOS is considered.

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“…Konrad et al presented a generic semantic model for fast ramp-up modeling, which combines human decisions data with machine data [20]. The use of semantic technology allows for context-sensitive exploration of past ramp-up cases and thus provides the foundation to employ learning techniques to discover more effective ramp-up strategies.…”

Section: Knowledge and Learning During Ramp-upmentioning

confidence: 99%

“…The ramp-up process includes a number of characteristics that render learning from experience a difficult, but a promising, task. Previous studies of the process show that operators follow a sequential procedure in which they apply changes to tune the system, based on monitoring its performance changes [20], [25]. The experience so gained contributes to enhancing the operator's learning curve, which helps in handling unexpected events, but it also delays the process.…”

Section: Symbiotic Human-machine Learning Approachmentioning

confidence: 99%

A Symbiotic Human–Machine Learning Approach for Production Ramp-up

Doltsinis

Ferreira

Lohse

2018

IEEE Trans. Human-Mach. Syst.

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Constantly shorter product lifecycles and the high number of product variants necessitate frequent production system reconfigurations and changeovers. Shortening ramp-up and changeover times is essential to achieve the agility required to respond to these challenges. This work investigates a symbiotic human-machine environment, which combines a formal framework for capturing structured ramp-up experiences from expert production engineers with a reinforcement learning method to formulate effective ramp-up policies. Such learned policies have been shown to reduce unnecessary iterations in human decision-making processes by suggesting the most appropriate actions for different ramp-up states. One of the key challenges for machine learningbased methods, particularly for episodic problems with complex state-spaces, such as ramp-up, is the exploration strategy that can maximize the information gain while minimizing the number of exploration steps required to find good policies. This paper proposes an exploration strategy for reinforcement learning, guided by a human expert. The proposed approach combines human intelligence with machine's capability for processing data quickly, accurately, and reliably. The efficiency of the proposed human exploration guided machine learning strategy is assessed by comparing it with three machine-based exploration strategies. To test and compare the four strategies, a ramp-up emulator was built, based on system experimentation and user experience. The results of the experiments show that human-guided exploration can achieve close to optimal behavior, with far less data than what is needed for traditional machine-based strategies.

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Enabling Fast Ramp-Up of Assembly Lines through Context-Mapping of Implicit Operator Knowledge and Machine-Derived Data

Cited by 8 publications

References 12 publications

Design of a self-learning multi-agent framework for the adaptation of modular production systems

Design of a self-learning multi-agent framework for the adaptation of modular production systems

Towards a Decision-support Framework for Reducing Ramp-up Effort in Plug-and-Produce Systems

A Symbiotic Human–Machine Learning Approach for Production Ramp-up

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